How to do data analysis for e commerce? How to get started with data analysis

First understand what data analysis is, which is defined as a series of analysis processes that concentrate, extract and refine the information hidden in a large number of seemingly disorganized data to find out the internal laws of the research object and provide decision support. Data, information, marketing decisions, and sales. Since it's decision support, data analytics helps us identify problems, analyze problems, and guide us to make the best marketing decision decisions.

Shopping malls are like battlefields, and data analysis is the radar in store business wars.

The role of data analysis:

Share the results of online activities, evaluate the performance (KPI) of relevant personnel, monitor the input-output (ROI) of promotion, find problems in customer service, marketing, etc., the future trend of the market, and help improve the UED.

2. Data analysis: about monitoring.

Many people will say, you don't have to enter the monitoring, don't you have records on the quantum? However, as everyone knows, the process of entry and monitoring is actually the process of analysis, and often the person who does data entry is the person who knows the overall situation of the company best. Regarding the best tools for monitoring data, there are only a few commonly used:

Data Rubik's Cube, Quantum Statistics, Promotion Backend, Others.

**Not much, but it takes a lot of effort to use proficient, proficient, and fully extract useful information from the data. Use quantum statistics to obtain the advantages and disadvantages of the store itself, use the data cube to look at the industry overview, evaluate the ROI from the promotion background, and add other data analysis tools from your own perspective, and finally effectively combine them.

1. Sales model (store operation overview).

2. Product model (product-oriented).

3. Promotion model (promotion-oriented).

3. Data analysis: about comparison.

Data analysis needs to be compared, which can be compared with others or industries, or compared with oneself in different time periods. For example, I compared with the industry's data this month, and found that the rest of the links are slightly higher than the industry average, only the customer unit price part is a short board, so the decision support provided should be to increase the same kind of baby recommendation and collocation work, as well as do more store activities to increase the unit price of customers.

For example: through the comparison between this week and last week, it was found that the sales fell seriously, and further analysis found that the sales of the industry did not decrease but increased.

4. Data analysis: about decomposition.

Decomposition is also an indispensable part of data analysis, especially in the future market** and traffic proportional distribution. To give you a simple example: now I'm going to ramp up my promotion efforts and increase sales by 20% within cost control.

First, use the formula "sales = traffic x conversion rate x unit value" to break down the sales, and use the control variable method to keep the conversion rate and unit value unchanged.

1. Analysis of sales status: mainly analyze the sales situation of this month, the completion of sales indicators of this month, and the comparison with the same period of last year (or last month). Through the analysis of this set of data, we can know the year-on-year sales trend and the gap between actual sales and plans.

2. Sales gross profit analysis: mainly analyze the gross profit margin and gross profit amount of this month, and compare with the same period last year. Through the analysis of this set of data, we can know the year-on-year gross profit situation and whether there is a deficiency in the gross profit of goods.

Users, products, channels. Those who are slightly involved in the e-commerce industry know that the only way to pry these "three mountains" is to "conduct data analysis". Data operation analysis is a skill that every "e-commerce person" needs to master, and without a specific industry framework, blind operation is a taboo for e-commerce operation.

First, the RFM model

By understanding the customers who have purchased in the first place, and describing the value of the customer by analyzing the customer's late purchase behavior, it is to continue to distinguish customers in terms of time, frequency, amount and other aspects, and analyze the data through this model.

**You can distinguish between members of various levels, iron members, bronze members or gold members. At the same time, for some customers who have not purchased for a long time, you can carry out some targeted marketing activities for them to activate these dormant customers. With the RFM model, membership differentiation can be achieved by grouping according to three different variables.

Second,RFM model

This should belong to a kind of data mining tool, a kind of correlation analysis, you can see which two goods are related, such as clothes and pants and other matching wearing methods, through the APRIORI algorithm to pick up regrets, you can get the relationship between the two goods, which can determine the display of goods and other factors, but also can be sold in sets of customers' purchase experience.

Third, SPSS analysis

It is mainly for the refined analysis of marketing activities, so that the marketing activities for customers are more targeted, and the customers in the database can also be analyzed for the goods that have been purchased by customers, such as which customers have purchased these goods at the same time, especially for the subdivision of e-commerce is becoming more and more refined, and the analysis of fine marketing is of great benefit to the marketing effect of the enterprise.

Clause.

Fourth, ** analysis

Data such as visits, page stays, etc., are all traffic indicators that are expected in the spring, and when the data is analyzed, the traffic and conversion rate.

It's also one of the ways to measure your work, and it's also crucial to understand how other data is changing through this metric.

Here are some of the reasons to learn about e-commerce data analytics:

Compared with the traditional retail industry, the biggest feature of e-commerce is that everything can be monitored and improved through data. Through the data, you can see where users come from, how to organize products to achieve a good conversion rate, how efficient you are in advertising, and so on. Every little change in data analysis is to improve your ability to make money a little bit, so the data analysis of e-commerce is particularly important.

E-commerce data analysis is to analyze the transaction behavior based on the user's transaction information, including purchase time, purchased goods, purchase quantity, payment amount, etc., to estimate the value of each customer in the letter segment and the possibility of expanding marketing for each customer. This analysis process is known as e-commerce data analysis.

There are seven important factors in e-commerce data analysis: E-commerce data analysis needs to be commercially sensitive; E-commerce conversion rate is the key, and ROI is the ultimate goal; setting of e-commerce data analysis measurement indicators; Analysis of the reasons for abnormal changes in certain indicators; Use data to analyze users' behavior habits; analysis of customers' purchasing behavior; E-commerce data analysis needs to focus on practical experience.

Compared with the traditional retail industry, the biggest feature of e-commerce is that everything can be monitored and improved through data. Through the data, we can see the problems of users coming from, how to organize products to achieve a good conversion rate, how efficient the advertising is, and so on. Every little change based on data analysis is a little bit to improve the ability to make profits, so the data analysis of e-commerce is particularly important.

1. List methodIt is the most common method of recording and processing data to express data in a list according to certain rules. **The design requirements are clear, simple and clear, which is conducive to discovering the correlation between related quantities; In addition, it is also required to indicate the name, symbol, order of magnitude and unit of each quantity in the title column: according to or according to the needs of the training, the calculation columns and statistical columns other than the original data can also be listed.

2. Drawing method.

The graphing method can most prominently express the change relationship between various physical quantities. From the graph line, you can easily find some results required for experiments, and you can also represent some complex functional relationships graphically through certain transformations.

There are two main ways to generate charts and graphs: manual tabulation and automatic programmatic tabulation, where tabulation is done through the corresponding software, such as SPSS, Excel, MATLAB, etc. The survey data is entered into the program, and the final results are obtained by operating these software, and the results can be displayed in the form of charts or graphs.

Graphs and charts can directly reflect the results of the survey, which greatly saves the time of the designer, helps the designer to better analyze the products required by the market, and paves the way for further design. At the same time, these forms of analysis are also used in product sales statistics, so that the recent product sales can be given intuitively, and the future market sales can be analyzed in a timely manner.